Estimating the economic impacts of the first wave of COVID-19 in Pakistan using a SAM Multiplier Model

Moeen, Muhammad Saad; Haider, Zeeshan; Shikoh, Sania Haider; Rizwan, Noormah; Ejaz, Amna; Davies, Stephen; Rana, Abdul Wajid

doi:10.2499/p15738coll2.134273

Cited by 2 publications

(8 citation statements)

References 7 publications

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“…Our estimation of poverty rates, originally developed for the Moeen et al 2021 paper, is provided in Table 3 and Figure 1. Using the HIES and the GoP's CBN poverty line of PKR 45,312 per capita per year, we identified groups of poor families that varied by location and farm versus nonfarm employment.…”

Section: Resultsmentioning

confidence: 99%

“…HRN34 and HU34 are higher income households and were treated as exogenous in the analysis. Source: Authors' estimations using Moeen et al (2021).…”

Section: Near-poor Poor Nonpoormentioning

confidence: 99%

“…Urban households (HU1, HU2 and HU34) are also split by quartiles, but with Quartiles 3 and 4 combined. Source:Moeen et al (2021).…”

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confidence: 99%

“…The number of poor families also soared during the lockdown phase. Moeen et al (2021) noted that prior to the pandemic, poverty was already high, at 27% and 57 million people, while Markhof (2020) reports a headcount of 24.3%. At its peak in April, using methods of Pauw et al (2021), the rate may have risen to 43%, with an added 34 million persons in poverty.…”

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confidence: 99%

“…The Government of Pakistan (GoP) moved swiftly with policy and monetary measures to mitigate anticipated effects of COVID‐19 (Moeen et al, 2021). To reinvigorate the economy, the GoP announced numerous fiscal and administrative measures, such as easing lockdowns to restore food supply chains, providing a stimulus package of US$7 billion, and creating an accommodating monetary policy.…”

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confidence: 99%

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Assessing the impact of COVID‐19 and related interventions on poverty and economic growth in Pakistan: A structural path analysis

Davies

Quershi

Rana

et al. 2023

Applied Eco Perspectives Pol

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This study uses social accounting matrix multipliers and structural path analyses to estimate effects of COVID‐19 and related fiscal stimuli on five household groups. The COVID‐19 lockdown increased poverty in Pakistan by 15%, which was addressed using a $1.5 billion, digitally implemented Ehsaas Emergency Cash (EEC) program that reached 14.8 million poor households. The study's models show that the largest multipliers from Ehsaas program finance were in agriculture, as a 1 Rupee shock adds 0.225 Rupee income to households. About 30% of that gain was estimated to go to poor farm families. In contrast, our models find that construction and trade growth added three times as much income to poor nonfarm and urban households as to farm households. However, those sectors added only one third as much total income as agriculture. From the structural path analysis, the importance of capital assets in generating income was seen, as was the possibility of greater poverty reduction from sectors with proportionally fewer intermediate inputs and more value added.

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Section: Resultsmentioning

confidence: 99%

“…HRN34 and HU34 are higher income households and were treated as exogenous in the analysis. Source: Authors' estimations using Moeen et al (2021).…”

Section: Near-poor Poor Nonpoormentioning

confidence: 99%

“…Urban households (HU1, HU2 and HU34) are also split by quartiles, but with Quartiles 3 and 4 combined. Source:Moeen et al (2021).…”

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Assessing the impact of COVID‐19 and related interventions on poverty and economic growth in Pakistan: A structural path analysis

Davies

Quershi

Rana

et al. 2023

Applied Eco Perspectives Pol

Self Cite

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Subnational Income, Growth, and the COVID-19 Pandemic

Choudhary,

Dal Barco,

Haqqani

et al. 2024

The World Bank Economic Review

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Using real-time data and machine-learning methods, we produce monthly aggregates on gross national income (GNI) for 147 Pakistani districts between 2012 and 2021. We use them to understand whether and how the COVID-19 pandemic affected the growth and subnational distribution of income in Pakistan. Three findings emerge from our analysis. First, districts experienced a sizable decline in income during the pandemic, as their monthly growth rate dropped on average by 0.133 percentage points. Second, a larger income drop took place in districts with a higher COVID-19 incidence, which correspond to urban areas characterized by a higher population density. Third, COVID-19 caused a decline in income inequality across districts, with richer districts experiencing more negative income growth during the pandemic.

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Estimating the economic impacts of the first wave of COVID-19 in Pakistan using a SAM Multiplier Model

Cited by 2 publications

References 7 publications

Assessing the impact of COVID‐19 and related interventions on poverty and economic growth in Pakistan: A structural path analysis

Assessing the impact of COVID‐19 and related interventions on poverty and economic growth in Pakistan: A structural path analysis

Subnational Income, Growth, and the COVID-19 Pandemic

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